The use of driving simulators for training and for development of new vehicles is widely spread in the automotive industry. In the last decade, a few motorcycle riding simulators have been developed for similar purposes, with focus on maneuvering at high speed. This article presents the subjective and objective evaluation of a motorcycle riding simulator specifically for low speed longitudinal and lateral maneuvering, between 0 and 10 ms^–1. An experiment was conducted with and without platform motion, focusing on three maneuvers: acceleration from standstill, braking to standstill and turning at constant speed. Participants briefly evaluated the fidelity of the simulator after each maneuver and more extensively after each motion condition. Behavioral fidelity was evaluated using experimental data measured on an instrumented motorcycle. Overall, the results show that the participants could reproduce the selected maneuvers without falling or losing balance, reporting a sufficient level of simulator realism. In terms of subjective fidelity, platform motion had a positive effect on simulator presence, significantly increasing the feeling of being involved in the virtual environ0ment. In terms of behavioral fidelity, the comparison between the simulator and experimental results shows good agreement, with a limited positive influence of motion for the braking maneuver, which indicates that for this maneuver the use of motion is beneficial to reproduce the real-life experience and performance.

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Driving simulators have been extensively used over the last decades and technological advancements have propelled their development for cars, trucks and other vehicles with four (or more) wheels. This dissertation focuses on the use of driving simulators for two wheeled vehicles and in particular on the development and evaluation of a motorcycle riding simulator for low speed maneuvering. The reason to focus on low speed maneuvers is related to the unstable nature of motorcycles at low speeds. A dedicated riding simulator could be used to train riders to cope with vehicle instabilities and develop active safety systems that can help them to maintain the vehicle balanced and avoid falling. Existing riding simulators adopt simplified vehicle models to simulate motorcycle dynamics. In some cases, advanced non-linear models are adopted, but their validation is not always sufficiently described for the simulator application. Once the model has been integrated in the complete simulator, the results of its real-time simulation are used to provide feedback to the simulator rider through the cueing systems. Motion cueing is particularly interesting due to the peculiar vehicle dynamics of two wheelers. Different approaches are found in literature, however the applied motion cueing methods are not based on understanding of human motion perception. Finally, the riding simulator should also be validated for its usage in the specific application domain and its fidelity and behavioral validity are often neglected. In this thesis, specific aspects of development and validation of a riding simulator for low speed maneuvering are investigated. @en

Powered two-wheeler riders are frequently involved in crashes at intersections because an approaching car driver fails to give right of way. This simulator study aimed to investigate how riders perform an emergency braking maneuver in response to an oncoming car and, second, whether longitudinal motion cues provided by a motion platform influence riders' braking performance. Twelve riders approached a four-way intersection at the same time as an oncoming car. We manipulated the car's direction of travel, speed profile, and its indicator light. The results showed that the more dangerous the situation (safe, near-miss, impending-crash), the more likely riders were to initiate braking. Although riders braked in the majority of trials when the car crossed their path, they were often unsuccessful in avoiding a collision with the car. No statistically significant differences were found in riders' initiation of braking and braking style between the motion and no-motion simulator configurations.@en

Driving simulators are widely used for understanding human–machine interaction, driver behavior and in driver training. The effectiveness of simulators in this process depends largely on their ability to generate realistic motion cues. Though the conventional filter-based motion-cueing strategies have provided reasonable results, these methods suffer from poor workspace management. To address this issue, linear MPC-based strategies have been applied in the past. However, since the kinematics of the motion platform itself is nonlinear and the required motion varies with the driving conditions, this approach tends to produce sub-optimal results. This paper presents a nonlinear MPC-based algorithm which incorporates the nonlinear kinematics of the Stewart platform within the MPC algorithm in order to increase the cueing fidelity and use maximum workspace. Furthermore, adaptive weights-based tuning is used to smooth the movement of the platform towards its physical limits. Full-track simulations were carried out and performance indicators were defined to objectively compare the response of the proposed algorithm with classical washout filter and linear MPC-based algorithms. The results indicate a better reference tracking with lower root mean square error and higher shape correlation for the proposed algorithm. Lastly, the effect of the adaptive weights-based tuning was also observed in the form of smoother actuator movements and better workspace use.

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Optimization-based motion cueing algorithms based on model predictive control have been recently implemented to reproduce the motion of a car within the limited workspace of a driving simulator. These algorithms require a reference of the future vehicle motion to compute a prediction of the system response. Assumptions regarding the future reference signals must be made in order to develop effective prediction strategies. However, it remains unclear how the prediction of future vehicle dynamics influences the quality of the motion cueing. In this study two prediction strategies are considered. Oracle: the ideal prediction strategy that knows exactly what the future reference is going to be. Constant: a prediction strategy that ignores every future change and keeps the current vehicle’s linear accelerations and angular velocities constant. The two prediction strategies are used to reproduce a sequence of maneuvers between 0 and 50 km/h. A comparative analysis is carried out to objectively evaluate the influence of the prediction strategies on motion cueing quality. Dedicated indicators of correlation, delay and absolute error are used to compare the effects of the adopted prediction on simulator motion. Also the motion cueing mechanisms adopted by the different conditions are analyzed, together with the usage of simulator workspace. While the constant strategy provided reasonable cueing quality, the results show that knowledge of the future vehicle trajectory reduces the delay and improves correlation with the reference trajectory, it allows the combined usage of different motion cueing mechanisms and increases the usage of workspace.

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Inertial or motion cue plays a significant role for the achievement of an immersive feeling in driving simulators. The control loop is responsible for mimicking as close as possible the real vehicle accelerations as the inertial feedback to the driver, while keeping the motion system within its physical limitations. Several algorithms have been designed for this purpose, seeking for the optimal compromise between realistic accelerations fidelity and actuation dynamics restrictions. Motion cueing algorithms can hence vary depending on the dynamics of the maneuver, computational efficiency required and motion system configuration. Several algorithms can be found in literature, from the classical approach, based on a combination of high and low-pass filters on the vehicle accelerations, to more recent strategies based on Model Predictive Control (MPC). Assessing the success of one solution is a complex task that would involve the prototype implementation on hardware as well as the availability of several test subjects. Nevertheless the result would be a subjective evaluation of the performance. This study proposes instead a preliminary analysis of the performance of motion cueing algorithms with objective evaluation by means of a human motion perception model.@en